Bivariate choropleth map comparing the Black (blue) and Hispanic (red) populations in the United States, 2010 census; shades of purple show significant proportions of both groups.
A bivariate map or multivariate map is a type of thematic map that displays two or more variables on a single map by combining different sets of symbols.[1] Each of the variables is represented using a standard thematic map technique, such as choropleth, cartogram, or proportional symbols. They may be the same type or different types, and they may be on separate layers of the map, or they may be combined into a single multivariate symbol.
The typical objective of a multivariate map is to visualize any statistical or geographic relationship between the variables. It has potential to reveal relationships between variables more effectively than a side-by-side comparison of the corresponding univariate maps, but also has the danger of Cognitive overload when the symbols and patterns are too complex to easily understand.[2]: 331
^Nelson, J. (2020). Multivariate Mapping. The Geographic Information Science & Technology Body of Knowledge (1st Quarter 2020 Edition), John P. Wilson (ed.). DOI: 10.22224/gistbok/2020.1.5
^T. Slocum, R. McMaster, F. Kessler, H. Howard (2009). Thematic Cartography and Geovisualization, Third Edn. Pearson Prentice Hall: Upper Saddle River, NJ.
A bivariate map or multivariatemap is a type of thematic map that displays two or more variables on a single map by combining different sets of symbols...
with each other (forming a bivariate or multivariatemap) and with one or more reference map layers in a single map. For example, a cartogram technique could...
In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization...
as a particular multivariate distribution, may be expressed by a multivariate cumulative distribution function, or by a multivariate probability density...
The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models...
color-blind people ( colorblind-friendly) color space color depth 1D 2D: Multivariatemap, bivariate or trivariate 3D An axial color gradient (sometimes also...
An interesting map will no doubt engage a reader. Information richness or a map that is multivariate shows relationships within the map. Showing several...
according to an overall total amount. This approach is thus a form of multivariatemap. The most common technique, first appearing in the 1850s, is to start...
Multivariate cryptography is the generic term for asymmetric cryptographic primitives based on multivariate polynomials over a finite field F {\displaystyle...
cartogram (also called a value-area map or an anamorphic map, the latter common among German-speakers) is a thematic map of a set of features (countries,...
In probability, and statistics, a multivariate random variable or random vector is a list or vector of mathematical variables each of whose value is unknown...
posteriori probability (MAP) estimate is an estimate of an unknown quantity, that equals the mode of the posterior distribution. The MAP can be used to obtain...
The multivariate ENSO index, abbreviated as MEI, is a method used to characterize the intensity of an El Niño Southern Oscillation (ENSO) event. Given...
polynomial rings are defined up to a unique isomorphism.) In other words, a multivariate polynomial ring can be considered as a univariate polynomial over a smaller...
analysis may also be divided into linear and non-linear, and univariate and multivariate. A time series is one type of panel data. Panel data is the general class...
if it is analytic at each point in U. Osgood's lemma shows (using the multivariate Cauchy integral formula) that, for a continuous function f, this is equivalent...
approximations, using the fact that the differential of a multivariate function at a point is the linear map that best approximates the function near that point...
Multivariate Statistics: a Vector Space Approach. John Wiley and Sons. pp. 116–117. ISBN 0-471-02776-6. W J Krzanowski "Principles of Multivariate Analysis"...
numerous programming languages. Partial composition is possible for multivariate functions. The function resulting when some argument xi of the function...
Kyrtsou C., Labys W.; Labys (2007). "Detecting positive feedback in multivariate time series: the case of metal prices and US inflation". Physica A. 377...
The resultant of n homogeneous polynomials in n variables (also called multivariate resultant, or Macaulay's resultant for distinguishing it from the usual...
to grow exponentially, such as incomes or prices Multivariate normal distribution (aka multivariate Gaussian distribution), for vectors of correlated...
x_{n-1}]\right)[x_{n}].} So, most of the theory of the multivariate case can be reduced to an iterated univariate case. The map from R to R[x] sending r to itself considered...
where E {\displaystyle \operatorname {E} } denotes expected value. For multivariate distributions, the product tX is replaced by a scalar product of vectors...
It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. Its roots...
Multivariate kernel density estimation Multivariate normal distribution Multivariate Pareto distribution Multivariate Pólya distribution Multivariate...
scope for multivariate data: Bar charts can only display one or two variables at a time, making them less useful for displaying multivariate data. In such...
Global Information